Weighed linear models are typically a very general class of statistical decision models utilized for many applications, including statistical machine translation. A weighted linear model generally computes a score as a weighted sum of features of an input and a possible output, and selects the highest (or lowest) scoring possible output considered for a given input. In statistical machine translation the input typically is a sentence in a first language to be translated (e.g., Inuktitut), and the possible outputs are candidate translations of the input sentence in a second language (e.g., Guarani). The features for example, can be logarithms of estimated probabilities of particular segments of the input language translating as particular segments of the output language, logarithms of language model probabilities of the output language, or penalties for re-ordering segments of the output language with respect to the order of the corresponding segments of the input language.
Minimum error rate training generally refers to any technique for choosing weights for values of the features of the model so as to minimize an error metric on the outputs selected for training a training set of inputs for which correct outputs are known, or equivalently maximizing a quality metric for such a set. This process of choosing weights that minimize error on the training set can be very time consuming, taking hours or days, depending on the complexity of the model.
The subject matter as claimed is directed toward resolving or at the very least mitigating, one or all the problems elucidated above, and in particular, the claimed matter is directed towards speeding up the process of selecting weights that minimize error on the training sets.